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OASIS: an automated program for globalinvestigation of bacterial and archaeal insertionsequencesDavid G. Robinson1, Ming-Chun Lee1 and Christopher J. Marx1,2,*

1Department of Organismic and Evolutionary Biology and 2Faculty of Arts and Sciences Center for SystemsBiology, Harvard University, Cambridge, MA 02138, USA

Received March 31, 2012; Revised July 6, 2012; Accepted July 24, 2012

ABSTRACT

Insertion sequences (ISs) are simple transposableelements present in most bacterial and archaealgenomes and play an important role in genomic evo-lution. The recent expansion of sequenced genomesoffers the opportunity to study ISs comprehensively,but this requires efficient and accurate tools for ISannotation. We have developed an open-sourceprogram called OASIS, or Optimized AnnotationSystem for Insertion Sequences, which automatic-ally annotates ISs within sequenced genomes.OASIS annotations of 1737 bacterial and archaealgenomes offered an unprecedented opportunity toexamine IS evolution. At a broad scale, we foundthat most IS families are quite widespread;however, they are not present randomly acrosstaxa. This may indicate differential loss, barriers toexchange and/or insufficient time to equilibrateacross clades. The number of ISs increases withgenome length, but there is both tremendous vari-ation and no increase in IS density for genomes>2 Mb. At the finer scale of recently divergedgenomes, the proportion of shared IS content fallssharply, suggesting loss and/or emergence ofbarriers to successful cross-infection occursrapidly. Surprisingly, even after controlling for 16SrRNA sequence divergence, the same ISs weremore likely to be shared between genomes labeledas the same species rather than as differentspecies.

INTRODUCTION

The ever-increasing number of sequenced bacterial andarchaeal genomes provides a valuable opportunity tounderstand genome architecture and evolution.However, as new high-throughput sequencing methodsare developed, genome annotation quickly becomes thebottleneck for genomic research. Despite the developmentof various annotation programs for particular genomicfeatures, some important features such as insertion se-quences (ISs), the smallest and simplest autonomousmobile genetic elements, remain poorly annotated.ISs are short regions of DNA, usually between 700 and

3000 bp long that can move or copy themselves within agenome through self-transposition. The majority of ISspossess one or two open reading frames (ORFs) thatencode a transposase. These ORFs are surrounded bylinker regions that frequently end with short-terminalinverted repeats (IRs) ranging from 7 to 20 bp in length.Upon insertion, ISs often generate short directed repeatsfrom 2 to 14 bp immediately outside the IRs (1). Despiteconsiderable sequence divergence, ISs can be classifiedinto 26 families based on transposase homology andoverall organization, with some families divided furtherinto groups (2).Due to their movement within and across genomes, ISs

not only represent an important source of genetic vari-ation within genomes but also mediate horizontal genetransfer (HGT) among organisms and thus play a keyrole in genome evolution. Through transposition, ISscan interrupt the coding region of a gene, or disruptpromoter regions and alter gene expression. Given thatthere can be hundreds of copies of the same IS in agenome, they can also serve as sites of rearrangements

*To whom correspondence should be addressed. Tel: +1 617 496 8103; Fax: +1 617 495 8848; Email: [email protected]

The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.

Present addresses:David G. Robinson, Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA.Ming-Chun Lee, Department of Biochemistry, The University of Hong Kong, Pok Fu Lam, Hong Kong.

Nucleic Acids Research, 2012, 1–11doi:10.1093/nar/gks778

� The Author(s) 2012. Published by Oxford University Press.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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such as deletions, duplications and inversions throughhomologous recombination. On a practical level, trans-position has been a classic genetic technique to generatemutant alleles, generally loss-of-function insertions intogene products. ISs are thus often regarded as ‘selfish’genomic parasites proliferating at the cost of their hostand surviving only through horizontal transfer (3). Incontrast, experimental evolution in the laboratory hasdemonstrated that both transpositions (4,5) and re-arrangements (6–10) can also generate beneficial muta-tions and commonly represent a large portion of themutations identified in genomes following a period ofadaptation (11,12).The most comprehensive resource for ISs is the ISfinder

database (13). ISfinder annotations have been submittedby users and manually verified by the curators. Therefore,we assumed this database (as of 17 September 2011) to bean accurate set of all ISs, but incomplete due to the factthat genomes are being sequenced faster than they areannotated to this extent. The most common practice ingenome annotation has been to stop at the point oflabeling ORFs as ‘transposase’ or ‘integrase’ where suffi-cient homology was observed. Without classification ofISs into families and enumeration within genomes,neither broad-scale studies across taxa nor dynamicswithin closely related strains are possible.Previous approaches to annotate ISs across a wide

range of genomes have been either involved internal pipe-lines, require manual annotation, or have identified veryfew elements. An annotation program was used for ananalysis of 19 cyanobacterial and 31 archaeal genomes,but this has yet to be made publicly available as an auto-mated pipeline (2). ISSaga is a web application pipelinethat allows semi-automated annotation based on BLASTagainst the ISfinder database (14). While ISSaga providesuseful tools both for recognizing transposase ORFs andfor manually curating their edges, it cannot automaticallyidentify novel ISs not already present in ISfinder. Due tothe requirements for manual annotation of novel elementsand individual submission of genomes online, ISSaga isimpractical for comprehensive evolutionary analysesinvolving a large number of sequenced genomes. To ourknowledge, the only publically available program that hasbeen developed to identify new ISs is IScan (15). Thissystem utilizes BLAST with a single reference transposasesequence per IS family to locate novel transposases. Aninvestigation of ISs in 438 prokaryotic genomes concludedthat, given the limited number of ISs identified by thisapproach in most taxa, most IS families are quitelimited in their phylogenetic distribution (16). Wholetaxa, such as the a-proteobacteria, had almost no identi-fiable ISs via this method, whereas manual annotation hasfound genomes of many of these organisms, for exampleMethylobacterium extorquens (17), are rather denselypopulated with ISs.To perform a global investigation of ISs in bacterial and

archaeal genomes, we developed OASIS, or OptimizedAnnotation System for Insertion Sequences, a computa-tional tool for automated annotation of ISs. OASIS takesadvantage of widely available transposase annotations toidentify candidate ISs and then uses a computationally

efficient maximum likelihood method of multiple sequencealignment to identify the edges of each element. Thanksto its speed and flexibility, OASIS is capable not onlyof providing detailed IS information for a single genomebut also of annotating thousands of genomes withinhours, making it a valuable high-throughput tool for aglobal investigation of IS distribution across diversetaxa. We applied OASIS to 1737 sequenced bacterialand archaeal genomes. Through comparisons across1319 genomes to a benchmark of ISfinder annotations,OASIS performed approximately an order of magnitudebetter than IScan. With a more comprehensive andaccurate overall picture of IS distribution acrossgenomes, we were able to address the pattern of ISs evo-lution at scales ranging from all sequenced Bacteria andArchaea to diversity between sets of extremely closelyrelated lineages. Broadly, it is clear that IS familiesare quite widespread; however, they are not presentrandomly across phyla suggesting either borders toexchange or insufficient time to equilibrate. ConsideringIS number and density with regard to genome length, wefind tremendous variation, and with the exception ofgenomes <2Mb which have considerably fewer ISs,there is no positive correlation of IS density and genomelength. Finally, we find that the proportion of shared IScontent between recently diverged genomes drops quitequickly. Quite surprisingly, even after 16S divergence istaken into account, strains labeled as the same speciesshare more IS elements than those termed as being differ-ent, suggesting that IS exchange/survival is correlated withcurrent taxonomy.

MATERIALS AND METHODS

OASIS algorithm

OASIS is a free open source program implemented inPython. It is available at https://github.com/dgrtwo/OASIS. The input data in this study consisted of the1737 curated microbial genomes available in NCBI as of17 September 2011. These genomes were downloadedfrom NCBI in GenBank format from the publicly avail-able server. OASIS uses a library of 3703 ISs from ISfinder(13), in the form of an amino acid FASTA file, which areused to identify the family and group of each IS. Thesetransposase sequences were automatically downloadedfrom the ISfinder database on 17 September 2011.

ISs may occur once in a genome or may consist of a setof almost identical copies (2). ISs in a particular genomecan therefore be classified into two major groups in termsof copy number: multicopy and single-copy ISs. As thereare distinct levels of information available in each of thesecases, different algorithms perform better with each class.As such, we have designed OASIS to find these two groupsof ISs in two separate steps: first finding multicopy ISs andthen single-copy ISs. The overall schematic pipeline isshown in Figure 1.

OASIS identifies multicopy ISs in each genome byfinding conserved regions surrounding already-annotatedtransposase genes, which are identified by the word‘transposase’ in the ‘product’ field of GenBank files.

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The choice to require prior annotation limits use onunannotated genomic data; however, as publishedcomplete genomes are nearly always annotated to thislevel of detail via other pipelines, we feel that the compu-tational speed gained makes this decision quite worthwhile.One additional issue is that transposases are occasionallymisannotated as integrases, a term more commonly foundfor prophage. In order to avoid false positives fromprophage, which are generally present at single copies ina genome, genes containing the word ‘integrase’ are con-sidered only when present in multiple copies. Groups oftransposase genes that compose multiple copies of an ISare identified by length and sequence similarity. TwoORFsare considered similar if the identity between the se-quences, assuming no gaps, is >95%, This thresholdallows for small differences between the transposase se-quences in multiple copies of the same ISs, though itshould be noted that the great majority of cases ofmultiple copies are within 2% of each other. Genes thatfit this similarity threshold are combined into the same ISset. OASIS then uses a maximum likelihood algorithm,described in Supplementary Methods, to determine theedges of multicopy ISs based on conservation betweentheir surrounding regions.

To define the edges of single-copy ISs, we use anapproach first developed by IScan to find IRs aroundthe transposases, which are present for the majority ofISs (15). Briefly, a Smith–Waterman alignment, with amatch score of 1, a mismatch penalty of �3 and a gappenalty of �4, is performed comparing the regionupstream of the transposase (500 bp) with the reverse com-plement of the downstream region (500 bp) and thehighest match with a score >10 is assumed to be thepair of terminal IRs.Multicopy ISs are also checked for IRs using a Smith–

Waterman alignment with the same parameters,comparing the regions within 100 bp of each edge. If theIRs disagree with the edges determined by the maximumlikelihood algorithm, the edges are changed to match theIRs. If none are found, the region immediately inside theIS is checked with a mismatch penalty of only �1, in caseOASIS had already identified the edges correctly.As a result, each full-length IS is composed of a

transposase, a protein of one or more ORFs, andupstream and downstream sequences defined as linkerregions, typically ranging from 0 to 500 bp. The extremeedges of the IS can include a partially conserved IR oneach end ranging from 8 to 20 bp in length.

Figure 1. Flowchart portraying the full workflow of OASIS.

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Once groups of ISs are identified, BLASTN (NCBI) isused with one example from each set of ISs, selected basedon the presence of inferred IRs and the mode length of theISs, against the genome sequence to identify missing andpartial copies of the IS (for which there is often notransposase annotated). Thus, when present in multiplecopies OASIS finds partial ISs; it is not capable offinding these small IS fragments when no intact copywith an annotated transposase is present. RedundantBLAST results within a set are filtered out. OASIS thenuses hierarchical agglomerative clustering to identifygroups of IS lengths, clustering together groups whosemean lengths are closer than 100 bp apart. The modecluster is then assumed to be the true size of the IS andany fragments that are shorter than that threshold or600 bp are classified as partials. One intact ORF wasselected from each set of ISs and BLASTP was usedagainst the ISfinder database. If there were matches withan e-value <10�12, the IS set is classified according to thefamily and group of the best match, otherwise the IS set isclassified as ‘None’.The final output of OASIS includes two files for each

annotated genome: a file in GFF format of the ISs in eachgenome and each ISs characteristics, including thechromosome ID, start and end positions, direction,family and group, IRs (if found) and whether theelement is a partial element, and a file containing the nu-cleotide sequence of each identified IS and the amino acidsequence of each transposase in FASTA format.

Evaluation

The performance of OASIS was compared to IScan, usingISfinder database as the benchmark annotation. Eachgenome was then annotated using three methods:OASIS, IScan and a BLASTN against ISfinderdatabase. OASIS was performed using its defaultsettings and IScan annotations were obtained by runningIScan with its default settings on each genome. IScan usesBLAST with reference transposase sequences to findtransposases. In this run, we extracted the same referenceISs as in the original IScan analysis (16). Benchmark an-notations were obtained by mapping ISfinder sequencesonto genomes. BLASTN was used with ISfinder nucleo-tide sequences to search genomes in the same genus as thequery sequence’s origin. BLAST hits with identity <90%were removed, as were hits that were redundant (the startsand ends are within 100 bp of others in the same genome).Only 1311 of the genomes shared a genus with anyISfinder element and were included in the evaluationanalysis.Sensitivity and specificity were assessed by matching

elements between OASIS or IScan against the benchmarkdata set of ISfinder. Partial and truncated ISs are usuallynot annotated in ISfinder and thus were excluded from theevaluation analysis. As edges could be mis-annotatedeither by the BLAST from ISfinder or by one of the auto-mated annotation methods, any elements that overlappedwere considered matches, though the effect of requiringaccurate matches is shown in Supplementary Figure S1.

OASIS+ data set

While OASIS found two-thirds of the ISs obtained bymapping ISfinder elements back to genomes and foundnearly half as many new elements, there are many it didnot find (see ‘Discussion’ section). In order to make thesubsequent biological analysis as comprehensive aspossible, we combined the OASIS annotations with theISfinder annotations to form the OASIS+ data set. Toremove any redundancy between the annotation sets,any copy in ISfinder that overlapped a copy in OASISwas not included.

Phylogenetic analysis

Aligned 16S rRNA sequences were extracted fromRibosomal Database Project Release 10 on 14November 2011, by matching the genbank accessionnumber of each genome (18). For genomes that havemultiple annotated 16S sequences, the 16S sequencefrom each genome with the highest average similarity toall other 16S sequences was selected. In total, 1502 16Ssequences were extracted from RDP database and anextra 195 sequences were aligned first and added to theRDP alignment by ClustalW2 profile alignment (19).Fourteen sequences were deleted due to poor quality ofthe 16S sequences (e.g. multiple Ns) after manual verifica-tion. An overall phylogeny of the 1682 genomes was con-structed using Neighbor-joining method (20) anddisplayed by Interactive Tree of Life (21). The completetabulation of IS family content per genome is available inSupplementary Table S3.

We used SourceCluster, a pairwise distance test (22), totest the non-randomness of IS distribution across genomesfor each family. The sum of all pairwise distances between16S sequences of the genomes with at least one IS of thatfamily was calculated to represent the degree of clusteringeffect. To generate a corresponding null distribution foreach family, 1000 Monte Carlo simulations were per-formed by sampling the same number of pairs ofgenomes randomly and the position of the test statisticwithin the null distribution was used as the p-value.

IS content comparison for closely related genomes

To investigate shared IS content between closely relatedgenomes, we computed the numbers of matching ISsbetween each pair of genomes whose 16S divergence is<10%. Since the maximum 16S divergence within bacter-ial species is commonly cited to be 3%, we conservativelyexcluded genomes with 16S sequences that, on an average,diverged >3% with other genomes in their species toprevent cases of misannotation or poor alignments fromskewing our results (23). We used an alternative indexrather than the actual genomic position due to the lackof consistency in nucleotide coordinates between even re-cently diverged genomes. We considered ISs between twogenomes to match if their families are identical, theirlengths are within 200 bp of each other and if the ratioof the Levenshtein edit distance (the number of insertions,substitutions and deletions necessary to transform onestring into another (24) to the average length is <0.2.




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For each genome in the pair, we computed the number ofelements that match an element in the other genome. Wethen fit the following logistic model:

pi,j ¼1

1+e�zi,j

zi,j ¼ �0+�1 log10ðDi,jÞ+�2Si,j+BF

where pi,j is the probability an IS in one of the twogenomes i and j appears in the other, Di,j is the 16Sdivergence between the genomes, Si,j is whether the twogenomes are the same species and �F is a coefficientspecific to that IS family. We added 0.0001 to the diver-gence so that the logarithm could be taken, as the diver-gence can be zero. We performed logistic regression withthe number of shared and lost elements in each pair as thebinomial response variables. A higher coefficient indicatesthat a factor is associated with a higher probability of ISsharing between two genomes.

RESULTS AND DISCUSSION

OASIS performance

A total number of 41 821 copies representing 6829 uniqueIS sets were identified by OASIS in 1240 genomes out ofthe 1737 analyzed (Table 1 and Figure 2). Among those,16.4% of the unique IS sets are single-copy ISs. The re-maining sets belong to multicopy ISs and comprise 97.3%of the total copies, with the largest multicopy set in asingle genome containing 232 elements. OASIS took atotal of 9 h and 40min to annotate all 1737 genomes ona 4-core 2.8-Ghz processor, with a maximum per-genomerunning time of 6min.

To evaluate the performance of OASIS, we comparedthe program’s annotation to those of IScan by testing eachagainst benchmark annotations obtained from theISfinder database. Given that many genomes are not rep-resented in ISfinder, we considered sensitivity as a measureof annotation accuracy. Within the 1311 benchmarkedgenomes, OASIS found �66.0% of the 40 078 ISfinderannotations, whereas IScan found only 8.5%. OASISand IScan frequently identify ISs in ISfinder but disagreeabout the boundaries; OASIS annotated 42.3% of theISfinder annotations to within 10 bp of each edge, whileIScan found 3.1% at that error tolerance (the effect oferror tolerance on sensitivity is shown in SupplementaryFigure S1). The low sensitivity of IScan was not an artifactof our execution of the software, as the number and

distribution of ISs qualitatively matches the previouslyreported results (16,25). In addition, OASIS identified14 264 ISs that were not present in ISfinder, which isover half of the size of the database (Figure 3). Wemanually validated the novel ISs in a subset ofdata (1012 genomes, downloaded on February 2010) andfound that at least 85% of the 2625 unique IS typescontain real, intact transposes that had not submitted tothe ISfinder database, confirming the incompleteness ofISfinder database. Interestingly, despite the great differ-ence in numbers and sensitivity between OASIS andIScan, IScan found 1146 ISfinder elements that OASISdid not.The sensitivities of both OASIS and IScan depend very

strongly on the genus of the annotated genome.Supplementary Table S1 shows the individual sensitivitiesfor genera that include 10 or more genomes. IScan anno-tations were most sensitive in Escherichia genomes, finding25.1% of E. coli ISs, while finding no ISfinder elementsat all in several other large genera that contain ISs, suchas Streptococcus, Bacillus, Clostridium, Mycoplasma andMycobacterium. This specificity to particular taxa is likely

Figure 2. Venn diagram illustrating the identified number of ISs inISfinder, OASIS and IScan. OASIS found a total of 37 427 ISs(in copy numbers) in the 1319 benchmarked genomes while IScanonly found 2902 ISs, demonstrating a better performance of OASISover IScan. In addition to identifying 18 112 ISfinder elements,OASIS found 19 365 new ISs, indicating the advantage of OASIS infinding novel ISs.

Table 1. The size and characteristics of the four sets of IS annotations in this analysis

Annotation set Numberof copies

Numberof sets

Number ofgenomes

Number ofpartial elements

% of partialelements

Sensitivity(%)

Specificity(%)

OASIS 41 821 6829 1240 5655 13.5 66 54.7IScan 3252 428 257 335 10.3 8.5 77.5ISfinder BLAST 40 078 6012 932 10 584 26.4 – –OASIS+ 56 786 10 529 1347 11 022 19.4 – –

OASIS+ is the combination of the OASIS and ISfinder data sets. ISfinder and OASIS+ have no sensitivity or specificity since ISfinder is ourbenchmark and OASIS+ incorporates ISfinder.




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Figure 3. IS distribution across bacterial and archaeal genomes. (A) A neighbor-joining tree of 16S rDNA from 1682 completely sequenced bacterialand archaeal genomes, colored by phylum (proteobacteria are separated by class). Length of colored bars outside the tree is proportional to thenumbers of ISs identified by OASIS+, colored by 26 IS families (ISNCY: not classified yet; None: no hit in ISfinder database). The OASIS+ data setconsists of the combined annotations of OASIS and a BLASTN from ISfinder (see ‘Materials and Methods’ section, complete data in SupplementaryTable S3). (B) A heatmap showing the non-random occurrence of each IS family across genomes. Genomes are ordered according to the 16S tree in(A). The final four columns indicate the distribution of ISs present in the various data sets we compare.




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an artifact of IScan for identifying transposases, whichdepends upon BLAST homology with selected referencesequences (mostly from E. coli and other Proteobacteria).In terms of the total number of annotations and sensitivityto ISfinder, we found OASIS has better performance thanIScan overall and also within every genus with more than10 genomes.

Both OASIS and IScan have a much lower rate of de-tecting single-copy ISs than multicopy ISs, particularly atannotating their edges accurately. They find 10.2 and3.9% of single-copy ISs, respectively, whereas at anerror tolerance of 10 bp, these fall to only 6.0 and 1.3%.OASIS outperforms IScan by using sequence conservationbetween multiple copies to identify the edge of theelement, which gives a much higher signal. Amongelements that overlapped a benchmark element, OASISmissed the edges of the benchmark by a median of 2 bp,while IScan missed the edges by a median of 51 bp.

Despite the significant improvement of OASIS on ISannotation, the major limitation of our program is thatit depends on the quality of NCBI annotation. Throughmanual inspection, we found many elements do not haveannotated transposases in the NCBI genomes, whichmakes OASIS incapable of identifying them as candidates.OASIS is thus limited by the efficacy of transposase an-notation and identification algorithms, which can be in-accurate. Furthermore, that the sensitivity of OASIS

increases as the error tolerance increases indicates thatOASIS finds some elements but misannotates theiredges. It is also possible in some cases that the edges areincorrectly annotated in the benchmark data set.

IS element abundance and distribution

For further analysis of broad-scale patterns of ISs acrossbacteria and archaea we combined our OASIS annota-tions of ISs with those already in ISfinder to generate acollection, ‘OASIS+’, of 10 529 ISs (due to multicopy ISs,56 786 elements in total) across 26 IS families (Table 2).ISs appeared in 77.5% of the genomes, indicating a globalIS distribution across archaeal and bacterial domains(Figure 3 and Supplementary Table S2). This resultconcurs with the prevailing view of ISs being widespreadbut absent from a moderate number of microbialgenomes (26); however, it is in contrast to the resultsof IScan (Supplementary Figure S2). The proportionof ISs identified across taxa varied substantially. Largelydue to the large number of sequenced genomes from thesegroups, >60% of the ISs we found are from Proteo-bacteria and �20% are from Firmicutes. For phyla thathave more than 10 genomes, the average number pergenome ranges from 15 to 50 except the Chlamydiae/Verrucomicrobia and the Epsilonproteobacteria groups,which have fewer than 3 ISs per genome on average.

Table 2. The size and prevalence of each family in OASIS+, sorted by the number of IS copies in each

Family Numberof copies

Numberof IS sets

Numberof genomes

Number ofgenera

Number ofclasses

Number ofphyla

Average copynumber

IS3 9630 1902 799 246 28 18 5.06IS5 6692 1073 521 213 35 20 6.24None 3951 742 483 237 40 22 5.32IS110 3535 829 488 177 26 16 4.26IS256 2890 512 337 131 25 16 5.64IS200/IS605 2770 499 334 107 25 13 5.55IS630 2721 424 252 126 23 11 6.42ISL3 2262 426 268 85 19 12 5.31IS1 2247 152 110 27 11 7 14.78IS4 2245 521 295 118 21 12 4.31IS21 2225 386 251 115 18 12 5.76IS481 1984 270 207 96 17 10 7.35IS30 1916 355 259 76 15 8 5.4IS66 1654 360 204 87 17 9 4.59IS1182 1564 292 224 95 16 9 5.36ISAs1 1143 259 136 47 12 7 4.41IS1380 1075 133 106 52 13 6 8.08IS982 907 133 100 45 16 11 6.82IS701 882 126 91 57 18 11 7IS1634 862 140 89 54 20 11 6.16IS6 725 219 177 60 18 12 3.31IS1595 674 162 117 52 11 7 4.16ISNCY 660 182 148 80 22 13 3.63IS607 498 178 94 42 18 12 2.8ISH3 491 62 25 10 4 3 7.92IS91 314 94 72 35 13 6 3.34Tn3 213 85 72 47 8 4 2.51ISAzo13 56 13 13 9 7 5 4.31OASIS+ 56 786 10 529 1347 448 50 26 5.39All – – 1737 563 58 30 –

Note that many ISs had families that could not be identified and were recorded as None. The OASIS+ row describes the total number of taxa thatcontain ISs, while the ‘All’ row represents the total number of taxa used in the analysis.




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In addition, our results show no ISs in the 13 genomes inBuchnera or the 12 genomes in Prochlorococcus, which isconsistent with previous reports (27). This indicates theabsence of ISs in reduced genomes, which is consistentwith previous observations (28–32).Given that certain phyla, particularly those with smaller

genomes, have few IS elements we directly explored theeffect of genome size on IS number and density in theOASIS+ data set. In a previous study of ISs in 262genomes, a positive correlation was found between IScontent and genome size, which they attributed to thelower density of essential genes and thus of deleteriousinsertion sites (26). In the OASIS+ data, we found apositive Spearman’s coefficient of 0.46 (p& 0) betweeneach genome’s length and its number of ISs, though thisis weaker than the correlation found earlier using fewergenomes (Figure 4). Much of this correlation is driven bythe rarity of ISs in small genomes: the correlation amonggenomes of length >2Mb drops to just 0.21. The previousstudy also discovered a correlation between IS density andgenome length. While we discovered a weak correlationbetween density and length (Spearman’s r=0.25, p=0),it was entirely due to the rarity of ISs in small genomes.The correlation drops to �0.040 when only genomes>2Mb are considered. This suggests that ISs have diffi-culty surviving in genomes <2Mb, but that the density ofISs permitted does not further increase for genomes>2MB.

Beyond considering IS prevalence as a whole, we alsodetermined that there is some degree of clade specificityand a non-random distribution for nearly every individualIS family (Figure 3B and Supplementary Table S3). ISfamilies differ greatly in both their frequency and diversityof hosts, appearing in as few as 13 genomes (ISAzo13) toas many as 799 genomes (IS3) (Table 2). Each IS familyalso differs in its copy number per IS set (unique IS type).For example, IS1 has an average of 14.8 copies per setwhile Tn3 (technically a transposon that has additionalpassenger genes, but it is treated as an IS family byISfinder) has an average of only 2.5 copies per set, sug-gesting different replicative transposition rates across dif-ferent types of ISs. Interestingly, several families werelimited to only a single domain. IS1380, Tn3, ISAs1 andIS30 were present only in bacterial genomes, and IS3,despite being the most abundant IS family, was absentin all archaeal genomes except two. On the other hand,ISH3 was found only in Archaea. To statistically examinethe clustering effects of IS families, we applied phylogen-etic clustering analysis to compare the average pairwisedistance of 16S sequences within each IS family and testit against a null distribution (see ‘Materials and Methods’section). Consistent with the above observations, theresults showed 22 out of 26 families are significantlyphylogenetically clustered (p< 0.05) (SupplementaryTable S4), indicating a non-random distribution of ISsacross genomes. This non-random distribution of ISs

Figure 4. Plot of genome length versus number of ISs per genome, (Spearman’s r=0.459, p& 0). A best-fit line from a linear regression is shown,but it captures relatively little of the variation present (slope 8.42, R2=0.074). Note that the y axis is restricted to 100 for clarity, though some pointslie above the plot’s range.




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across genomes might be due to either boundaries to suc-cessful horizontal transfer, distinct selective forces facedby different IS families in different taxa, or recent emer-gence and insufficient time to equilibrate across taxa.

IS element survival in closely related genomes

The large number of taxa in our OASIS+ data set thatrepresent closely related genomes (strains within thesame species or genus) allowed us to address the extentto which IS turnover is seen at a finer scale. Previousstudies have determined that IS counts can vary widelyeven between closely related genomes (26,33,34). Mostof these studies concluded that ISs are short lived innatural lineages and that rapid HGT is required forthem to persist. To investigate the short-term proliferationand survival of ISs, we compared the fraction of ISsshared in each pair of closely related genomes with their16S divergence. The results show that the probability of anIS being shared between genomes decreases dramaticallywith increasing 16S distance: an IS has a 35.1% chance ofbeing shared between two genomes that have identical 16Ssequences, while it has only a 0.13% chance of beingshared between two genomes with 9–10% 16S sequencedivergence. Also notably, the probability of an IS beingshared between two genomes in the same species is 24.7%,while two genomes of different species within 10% 16Sdivergence have only a 2.2% chance of being shared.Figure 5 compares the divergence between each genomicpair to the percentage of ISs shared between them, forboth intraspecies and interspecies pairs and shows thatprobability of sharing decreases very quickly withincreasing divergence. This rapid decrease in shared IScontent during the early divergence of genomes could be

due to a decline in the probability of vertical inheritance orin the chance of being acquired horizontally. Figure 5 alsoshows for a given degree of 16S sequence divergence, pairsof taxa that are defined as the same species have moresimilar IS content than if they are defined as differentspecies. As an example, the probability of an IS beingshared between two Escherichia genomes is 17.4% andthe probability of being shared between two Shigellagenomes is 41.1%, while the probability of being sharedbetween an Escherichia genome and a Shigella genome isonly 4.7%. Shigella is a polyphyletic genus completelywithin the Escherichia genus (35) (the average 16S diver-gence between the two genera is 1.6%), so the difference inIS content might reflect biological differences rather thanjust the time since divergence.In order to quantify the effect of these factors on the

probability of shared IS content, we performed logisticregression to predict the proportion of matched IScopies between two genomes, using the log of the 16Sdistance, IS family and whether a pair was within asingle species. Different IS families have very differentprobabilities of being shared between closely relatedgenomes (Supplementary Table S5). Families such asIS1595 and IS1380 were particularly likely to appear inmultiple closely related genomes, while families such asIS91, Tn3 and IS66 were unlikely to be shared. Whilethe log genomic distance was found to have a very signifi-cant effect, particularly important was whether the twogenomes were considered to be part of the same species(p& 0). The intraspecies factor had a logistic regressioncoefficient of 1.85±0.01, which can be interpreted as anodds ratio of e1.85=6.36, indicating that there is a strongeffect of being defined as part of the same species on top ofthe effect of the pairwise 16S distance. The wide range of

Figure 5. A plot of the probability of two genomes sharing an IS copy compared to their 16S distance, for: (A) intraspecies and (B) interspeciespairs. An average using a Nadaraya–Watson kernel smoother with a bandwidth of 1% is shown in red. Note that at all 16S sequence distances theintraspecies value remains at least 2-fold higher than interspecies.




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coefficients among IS families shows that family does havea large effect on the probability of being shared, suggest-ing that elements in different families have differentprobabilities of net gain or loss.Part of the difference causing certain IS families to be

shared across closely related genomes can be attributed totheir differing copy numbers, For example, the IS familywith the lowest average copy number, Tn3, has one ofthe lowest coefficients in the regression and there-fore the lowest probability of IS sharing. Also importantis the effect of sequencing bias, as a family’s presence inhighly sequenced taxa allows its shared presence in closelyrelated pairs to be observed. While the effects of distanceand species are controlled for in the logistic regression, thehighly sequenced taxa can still inflate the estimates. Forexample, IS1380 has one of the highest coefficients in theregression, but >80% of the pairs that share IS1380elements are within closely related genomes in theAcetobacter pasteurianus or Streptococcus pneumoniaspecies. If those species had not been highly sequencedthe tendency would not have been discovered. However,other families, such as IS256 and IS200/605, are found tohave shared pairs in a wider variety of taxa. OASIS mightalso be capable of annotating some families more accur-ately than others, as its accuracy is affected by theproperties such as the presence of IRs and the level ofconservation between copies. The various reasons thatIS families have different rates of being shared acrossclosely related genomes deserves further study.

CONCLUSIONS

As the sequencing technology progresses, the need foruser-friendly, high-throughput annotation systems con-tinues to grow. We developed OASIS, an automated an-notation system for ISs, which is capable not only ofproviding detailed IS information for a single genome,but also of annotating thousands of genomes withinhours. A tradeoff inherent to computationally efficientautomated annotation at this scale is that, althoughOASIS fares better than previous software platforms,some ISs present in the manually curated ISfinderdatabase were missed. In developing our algorithms weerred on the side of caution, trying to minimize false posi-tives so that these would not be further propagated.By applying OASIS for high-throughput IS annotation

across genomes, this study examined IS evolution at bothbroad and narrow phylogenetic scales. Looking acrossall taxa, we revealed a nearly global distribution of ISsacross both Bacteria and Archaea and a non-randomnessof IS family distribution across taxa. Interestingly, withthe fuller OASIS+data set it becomes clear that, althoughsmall genomes <2Mb have relatively few ISs, beyond thissize the density of ISs per genome size is relativelyconstant. The clearest finding of ISs versus genome size;however, is just how large the variation is for any givengenome size, in accord with the variance previously notedwithin a single species (31). At a finer scale of closelyrelated genomes, we found that the probability of an ISbeing present drops precipitously, indicating either rapid

loss and/or emergence of increasing barriers to successfulexchange across diverging lineages. We also found, to oursurprise, that for a given 16S divergence, strains con-sidered to be of the same species have greater IS contentsimilarity than strains that are named as different species.This perhaps suggests that our current bacterial taxonomymanages to capture some real differences, at least as far asthey relate to differential exchange and survival of ISsconditions and thresholds relevant for IS survival andhorizontal transfer. The availability of the OASISplatform will hopefully aid in future work to tease apartthe individual contributions of IS loss and exchange thatgive rise to these global patterns and to explore other bio-logical and evolutionary characteristics of ISs.

SUPPLEMENTARY DATA

Supplementary Data are available at NAR Online:Supplementary Tables 1–5, Supplementary Figures 1 and2 and Supplementary Methods.

FUNDING

Taiwan Ministry of Education [Study Abroad Scholarshipto M.-C.L.]; Harvard University Microbial SciencesInitiative [fellowship to D.G.R.]; Harvard University[Harvard Merit Fellowship to M.-C.L. and set-up fundsto C.J.M.]. Funding for open access charge: HarvardUniversity.

Conflict of interest statement. None declared.

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1

Supplementary Table 1. Sensitivity of OASIS and IScan within each genus for all genera containing at

least 10 genomes.

Supplementary Table 2. The distribution of ISs in the OASIS+ dataset across phyla. Phyla were

determined by a search in the Entrez Taxonomy database, and sorted by the total number of genomes.

The sensitivity was determined against the ISfinder benchmark annotations.

Supplementary Table 3. The number of IS elements in each family in each genome, according to the

OASIS+ dataset; used to generate Figure 3B.

Supplementary Table 4. Results of a Monte Carlo test (see Methods) to detect phylogenetic clustering in

each family.

Supplementary Table 5. Logistic regression coefficient estimates and p-values for the effect of each

family, the log of the genomic distance, and whether two genomes are of the same species, sorted by the

coefficient size.

Supplementary Figure 1. The sensitivity of OASIS and IScan as a function of different error tolerances.

Supplementary Figure 2. 16S tree showing the distribution of IScan annotated ISs in 1 682 genomes.

This tree is analogous to Figure 3A, which shows the distribution of the OASIS+ dataset. Note that while

IScan identifies ISs in several genera, including Escherichia, Shigella, and Xanthomonas, it identifies

none or almost none in the vast majority of taxa, which is contradicted by the OASIS+ dataset.

Supplementary Table 1

Genus Number of genomes OASIS IScan ISfinder OASIS Sensitivity IScan SensitivityStreptococcus 74 1211 2 1190 0.551260504 0Escherichia 48 1529 582 2603 0.67038033 0.251248559Bacillus 44 605 1 481 0.467775468 0Clostridium 44 615 0 397 0.901763224 0Lactobacillus 38 1553 31 562 0.800711744 0.053380783Mycoplasma 37 429 1 467 0.957173448 0Helicobacter 36 28 1 29 0.24137931 0Staphylococcus 34 470 64 478 0.80125523 0.131799163Mycobacterium 29 1068 0 1207 0.755592378 0Burkholderia 29 1478 91 1568 0.888392857 0.030612245Pseudomonas 24 543 70 459 0.79956427 0.150326797Shewanella 24 952 43 1069 0.857811038 0.03554724Bifidobacterium 23 255 0 194 0.62371134 0Salmonella 23 238 47 282 0.656028369 0Yersinia 19 1788 26 1784 0.938340807 0Neisseria 18 481 0 685 0.750364964 0Corynebacterium 18 195 4 108 0.305555556 0Vibrio 16 128 36 176 0.568181818 0.068181818Rickettsia 15 171 0 46 0.956521739 0Listeria 15 5 0 17 0.117647059 0Haemophilus 14 36 1 26 0.576923077 0Campylobacter 14 7 0 0 -‐ -‐Sulfolobus 13 653 0 667 0.676161919 0Francisella 13 469 0 620 0.648387097 0Brucella 13 203 0 239 0.790794979 0Acinetobacter 11 474 2 431 0.974477958 0Xanthomonas 10 913 174 1457 0.569663693 0.182566918

Supplemental Table 2

Phylum Total genome number Genomes with ISs IS Sets Total IS Number Median IS Number Minimum IS Number Maximum IS NumberFirmicutes 378 334 2346 11223 20 0 219Gammaproteobacteria 365 314 2797 18055 21 0 811Alphaproteobacteria 166 131 1223 6136 14 0 238Actinobacteria 164 130 1223 4687 14 0 373Betaproteobacteria 113 101 979 5600 35 0 483Euryarchaeota 75 47 380 1686 5 0 184Bacteroidetes/Chlorobi group 72 54 282 1285 7.5 0 120Epsilonproteobacteria 62 31 44 183 0.5 0 24Tenericutes 46 28 91 829 5 0 182Deltaproteobacteria 45 40 266 1075 17 0 80Cyanobacteria 42 21 260 2067 5.5 0 648Chlamydiae/Verrucomicrobia group 41 8 28 120 0 0 48Crenarchaeota 37 17 204 1183 0 0 238Spirochaetes 37 21 69 660 4 0 139Chloroflexi 15 14 50 346 13 0 114Deinococcus-‐Thermus 14 13 99 540 27.5 0 181Thermotogae 12 9 33 180 5 0 60Aquificae 10 6 17 156 3 0 68Fibrobacteres/Acidobacteria group 7 5 29 156 10 0 62Fusobacteria 5 4 15 76 16 0 35Planctomycetes 5 3 9 53 3 0 37Deferribacteres 4 4 34 266 52 21 141Other 3 2 2 2 1 0 1Synergistetes 3 1 3 3 0 0 3Dictyoglomi 2 1 1 2 1 0 2Elusimicrobia 2 1 1 1 0.5 0 1Nitrospirae 2 2 7 15 7.5 3 12Thermodesulfobacteria 2 1 4 22 11 0 22Chrysiogenetes 1 1 4 30 30 30 30Gemmatimonadetes 1 1 6 12 12 12 12Korarchaeota 1 0 0 0 0 0 0Nanoarchaeota 1 0 0 0 0 0 0Thaumarchaeota 1 0 0 0 0 0 0unclassified Archaea 1 1 14 80 80 80 80unclassified Bacteria 1 0 0 0 0 0 0unclassified Proteobacteria 1 1 9 57 57 57 57Total 1737 1347 10529 56786 -‐ -‐ -‐

Supplemental Table 3Species IS1182 ISH3 IS1595 ISL3 IS1634 IS1380 IS200/IS605 IS630 ISNCY Tn3 ISAs1 IS30 ISAzo13 IS481 None IS701 IS256 IS21 IS91 IS607 IS982 IS3 IS1 IS110 IS6 IS5 IS4 IS66Acaryochloris_marina_MBIC11017_uid12997 0 0 0 32 0 1 0 63 0 5 4 0 0 0 4 27 8 3 0 0 0 5 28 0 1 45 23 0Acetobacter_pasteurianus_IFO_3283_01_42C_uid31141 0 0 0 0 0 55 0 0 2 0 0 0 0 3 0 18 0 2 0 0 0 28 0 1 0 32 0 0Acetobacter_pasteurianus_IFO_3283_01_uid31129 0 0 0 0 0 61 0 0 2 0 0 0 0 4 0 19 0 2 0 0 0 29 0 1 0 34 0 1Acetobacter_pasteurianus_IFO_3283_03_uid31131 0 0 0 0 0 62 0 0 2 0 0 0 0 5 0 17 0 2 0 0 0 29 0 1 0 34 0 1Acetobacter_pasteurianus_IFO_3283_07_uid31133 0 0 0 0 0 61 0 0 2 0 0 0 0 4 0 18 0 2 0 0 0 29 0 1 0 34 0 1Acetobacter_pasteurianus_IFO_3283_12_uid32203 0 0 0 0 0 61 0 0 2 0 0 0 0 4 0 17 0 2 0 0 0 29 0 1 0 34 0 1Acetobacter_pasteurianus_IFO_3283_22_uid31135 0 0 0 0 0 62 0 0 2 0 0 0 0 5 0 17 0 2 0 0 0 29 0 1 0 34 0 1Acetobacter_pasteurianus_IFO_3283_26_uid31137 0 0 0 0 0 62 0 0 2 0 0 0 0 5 0 17 0 2 0 0 0 29 0 1 0 34 0 1Acetobacter_pasteurianus_IFO_3283_32_uid31139 0 0 0 0 0 61 0 0 2 0 0 0 0 4 0 17 0 2 0 0 0 29 0 1 0 34 0 1Acetohalobium_arabaticum_DSM_5501_uid32769 0 0 0 0 0 0 0 0 1 0 0 0 0 0 6 0 0 0 0 14 0 0 0 0 0 0 2 0Acholeplasma_laidlawii_PG_8A_uid19259 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Achromobacter_xylosoxidans_A8_uid48393 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 2 0 0 0 2 0 0Acidaminococcus_fermentans_DSM_20731_uid33685 3 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0Acidianus_hospitalis_W1_uid60875 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Acidilobus_saccharovorans_345_15_uid39927 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Acidimicrobium_ferrooxidans_DSM_10331_uid29525 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0Acidiphilium_cryptum_JF-‐5_uid15753 0 0 4 7 2 3 0 0 0 3 1 0 0 0 0 2 15 8 0 0 0 0 0 3 4 3 0 0Acidiphilium_multivorum_uid60101 0 0 23 0 5 4 0 0 0 4 11 0 0 0 0 0 3 6 2 0 0 2 0 8 1 14 6 0Acidithiobacillus_caldus_SM_1_uid63223 1 0 0 41 0 0 4 5 0 0 0 0 0 2 0 0 0 14 0 0 0 0 0 0 0 25 0 8Acidithiobacillus_ferrivorans_SS3_uid61509 0 0 0 5 7 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 7 0 4 0 0 0 1Acidithiobacillus_ferrooxidans_ATCC_23270_uid53 0 0 0 0 0 0 1 0 0 0 0 0 0 0 3 0 1 2 0 2 0 2 0 10 0 0 0 4Acidithiobacillus_ferrooxidans_ATCC_53993_uid16689 0 0 0 0 0 0 0 0 0 0 0 0 0 3 24 0 0 5 0 0 0 2 0 3 0 0 0 5Acidobacterium_MP5ACTX9_uid47621 0 0 0 9 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 5 0 9 0 26 0 0Acidobacterium_capsulatum_ATCC_51196_uid28085 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 2 0 0 0 0 0 2 0 4 0 0Acidothermus_cellulolyticus_11B_uid16097 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Acidovorax_JS42_uid15685 0 0 4 7 0 0 0 17 0 6 0 0 0 0 6 1 5 8 0 0 0 1 0 19 0 26 0 3Acidovorax_avenae_ATCC_19860_uid37867 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 23 0 0Acidovorax_avenae_citrulli_AAC00-‐1_uid15708 0 0 0 0 0 0 0 0 0 11 0 0 0 19 0 0 0 0 0 0 0 29 0 10 0 7 0 0Aciduliprofundum_boonei_T469_uid38403 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0Acinetobacter_DR1_uid46105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Acinetobacter_baumannii_1656_2_uid42153 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 2 1 0 0 24 18 0Acinetobacter_baumannii_AB0057_uid21111 0 0 0 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 3 2 7 0Acinetobacter_baumannii_AB307_0294_uid30993 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Acinetobacter_baumannii_ACICU_uid17827 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 4 2 0 0 0 0 0Acinetobacter_baumannii_ATCC_17978_uid17477 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 2 1 0Acinetobacter_baumannii_AYE_uid28921 0 0 0 2 0 0 0 0 0 0 0 0 0 0 3 0 0 0 2 0 0 1 0 0 3 1 24 0Aeropyrum_pernix_uid211 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Acinetobacter_baumannii_SDF_uid13001 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 165 0 0 0 0 176 0 0Acinetobacter_baumannii_TCDC_AB0715_uid62279 0 0 0 0 0 0 0 0 0 0 0 2 0 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Xylella_fastidiosa_uid271 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0Yersinia_enterocolitica_8081_uid190 2 0 0 0 0 0 3 0 0 0 0 0 0 0 1 0 1 0 0 0 0 17 0 14 0 0 11 0Yersinia_enterocolitica_palearctica_105_5R_r__uid53341 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 5 0 0 0 4 1 51 1 0 0 0Yersinia_enterocolitica_palearctica_Y11_uid60483 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 6 0 0 0 6 0 56 1 0 0 0Yersinia_pestis_A1122_uid67155 0 0 0 0 0 0 63 1 0 0 0 0 0 0 0 0 22 47 0 0 0 8 1 1 0 0 0 0Yersinia_pestis_Angola_uid16067 0 0 0 0 0 0 97 0 0 0 0 0 0 0 0 0 38 48 0 0 0 11 1 1 1 0 0 0Yersinia_pestis_Antiqua_uid16645 0 0 0 0 0 0 64 0 0 0 0 0 0 0 0 0 22 74 0 0 0 9 1 1 1 0 0 0Yersinia_pestis_CO92_uid34 0 0 0 0 0 0 64 0 0 0 0 0 0 0 0 0 22 48 0 0 0 9 1 1 1 0 0 0Yersinia_pestis_D106004_uid36507 0 0 0 0 0 0 60 0 0 0 0 0 0 0 0 0 22 32 0 0 0 9 1 1 1 0 0 0Yersinia_pestis_D182038_uid36545 0 0 0 0 0 0 62 0 0 0 0 0 0 0 0 0 22 36 0 0 0 9 1 1 1 0 0 0Yersinia_pestis_KIM_10_uid288 0 0 0 0 0 0 51 1 0 0 0 0 0 0 0 0 20 37 0 0 0 10 1 1 0 0 0 0Yersinia_pestis_Nepal516_uid16646 0 0 0 0 0 0 61 1 0 0 0 0 0 0 0 0 24 32 0 0 0 10 1 1 0 0 0 0Yersinia_pestis_Pestoides_F_uid16700 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 22 26 0 0 0 9 1 1 0 0 0 0Yersinia_pestis_Z176003_uid36547 0 0 0 0 0 0 61 0 0 0 0 0 0 0 0 0 22 31 0 0 0 8 1 1 1 0 0 0Yersinia_pestis_biovar_Medievalis_Harbin_35_uid30505 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 22 34 0 0 0 8 1 1 1 0 0 0Yersinia_pestis_biovar_Microtus_91001_uid10638 0 0 0 0 0 0 44 0 0 0 0 0 0 0 0 0 23 35 0 0 0 9 1 1 1 0 0 0Yersinia_pseudotuberculosis_IP32953_uid12950 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 7 5 0 0 0 8 1 0 1 0 0 0Yersinia_pseudotuberculosis_IP_31758_uid16070 0 0 0 0 0 0 15 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 2 0 0 0 0 0Yersinia_pseudotuberculosis_PB1__uid28745 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 8 6 0 0 0 9 1 0 0 0 0 0Yersinia_pseudotuberculosis_YPIII_uid28743 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 5 0 0 0 0 2 1 0 0 0 0 0Zunongwangia_profunda_SM_A87_uid38641 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 16 0 2 0 0 2 2Zymomonas_mobilis_ATCC_10988_uid30987 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 34 0 0Zymomonas_mobilis_NCIMB_11163_uid34821 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Zymomonas_mobilis_pomaceae_ATCC_29192_uid34927 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0alpha_proteobacterium_IMCC1322_uid28081 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0butyrate_producing_bacterium_SM4_1_uid45955 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0butyrate_producing_bacterium_SS3_4_uid39155 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0butyrate_producing_bacterium_SSC_2_uid45957 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0cyanobacterium_UCYN_A_uid30917 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0halophilic_archaeon_DL31_uid52855 0 31 0 0 0 0 13 0 5 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 2 1 4 16marine_bacterium_HP15_uid46089 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0uncultured_Termite_group_1_bacterium_phylotype_Rs_D17_uid20871 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0uncultured_methanogenic_archaeon_RC-‐I_uid19641 0 0 0 5 6 0 0 1 4 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0


Family Test Statistic Control P-‐valueIS1182 0.238569796 0.282524132 0ISH3 0.223926818 0.283855563 0IS1595 0.249433554 0.282423267 0ISL3 0.243848718 0.282349206 0IS1634 0.296352681 0.282691323 0.918IS1380 0.237200357 0.282375409 0IS200/IS605 0.267753244 0.28268475 0IS630 0.273797685 0.282652734 0.053Tn3 0.218156373 0.281940996 0ISAs1 0.20109524 0.282410971 0IS30 0.236087124 0.282203052 0ISAzo13 0.270793522 0.282552668 0.364IS481 0.246110155 0.282473862 0IS701 0.25997134 0.282887453 0.007IS256 0.267271578 0.282235139 0.001IS21 0.249356118 0.282113021 0IS91 0.198051999 0.282862756 0IS607 0.294306711 0.282858576 0.885IS982 0.261764268 0.282381155 0.012IS3 0.241419414 0.282345855 0IS1 0.196650011 0.281866199 0IS110 0.256266433 0.282405487 0IS6 0.265807761 0.282547972 0.006IS5 0.274152533 0.282473901 0.006IS4 0.244476031 0.282728744 0IS66 0.247286538 0.282069397 0


Coefficient Standard Error P-‐valueIS1595 3.1284 0.0676 0IS1380 2.9623 0.068 0ISAs1 2.2423 0.0601 0IS1 2.1932 0.0589 0IS6 2.1668 0.0657 0IS701 2.1416 0.0779 0IS30 2.1075 0.0638 0IS1634 1.9196 0.1306 0IS5 1.8897 0.0598 0Intraspecies 1.8467 0.01 0IS200/IS605 1.8111 0.0601 0IS3 1.6914 0.0587 0ISL3 1.5838 0.0647 0IS256 1.5703 0.0629 0ISH3 1.5003 0.0859 0IS110 1.462 0.0618 0IS1182 1.2534 0.0653 0IS481 1.0648 0.0731 0ISAzo13 1.0254 0.7121 1IS4 0.8917 0.0643 0IS630 0.817 0.0676 0IS982 0.7425 0.1132 0IS21 0.5041 0.0652 0IS607 0.3315 0.1644 1IS66 0.2476 0.0659 0.005Tn3 -‐0.0327 0.3238 1Distance -‐0.3834 0.0025 0IS91 -‐0.9056 0.1412 0Intercept -‐6.9336 0.0595 0

0 20 40 60 80 100Tolerance: maximum distance of IS ends from benchmark ends

0.0

0.1

0.2

0.3

0.4

0.5

0.6Sensi

tivit

yOASIS and IScan sensitivity by error tolerance

OASISIScan

OASIS finds multiple-copy IS elements in each genome by identifying conserved regions surrounding transposase genes. First, groups of already-annotated transposase genes that could compose multiple copies of an IS were identified by length and sequence similarity. Those that fit a high similarity threshold were assumed to be in the same group.

The edges of each element were then found by finding a region of conservation around each group of transposases. The windows upstream and downstream of a group of transposases were compared. We are thus given a set of sequences and asked to find the edge at which conservation ends, allowing for mismatches within IS elements, in a more computationally efficient matter than a multiple alignment. The edge of conservation was found via the following maximum likelihood approach.

We assumed that upstream and downstream regions each consist of a contiguous conserved region (within the IS) and an unconserved region (outside the IS). For computational efficiency, it was assumed that there were no gap mutations in the conserved region. Consider the set of upstream or downstream n-windows, in a group that contains m transposases. Let k be the length of the conserved region- the length which the IS element extends past the transposase on this side.

The first k characters of the sequences each come from multinomial distributions that are identical across the m sequences and are biased heavily towards a single nucleotide (the true sequence of the IS element). Let cj be the consensus nucleotide for the jth character of the true sequence, and let xi,j represent the jth character of the ith sequence.

xij ~Multinom(pcj) if i k

Multinom(pb) if i k

where pcj represents the distribution of a conserved nucleotide with consensus value cj, and pb represents a background distribution vector (the distribution of nucleotides in unconserved regions). pc can be any of 4 vectors that are biased towards a consensus nucleotide, with some probability of error. Let c, ε be adjustable parameters specifying the probability of a consensus match or an error in a conserved region.

The value of k is then found that maximizes the likelihood of the set of sequences. The likelihood is found by a dynamic programming algorithm as follows. For each nucleotide N ∈ {A, G, C, T }, let Ni be the count of nucleotides N in x1...m,i. Also let Mi be the count of the most common nucleotide (the MLE estimate of the consensus nucleotide in a conserved region). The log-likelihood function is then

l(k) log(m!

Ai!Ci!Ti!Gi!cM i

i1

k

mM i

) log(m!

Ai!Ci!Ti!Gi!(1

4)m

i k1

n

)

Our estimate of k is thus

ˆ k argmax

kl(k)

OASIS computes this through a dynamic programming algorithm. The putative edge of the IS element is then marked as nucleotides from the edge of the transposase. This process is repeated for the windows upstream and downstream of the repeated transposase, for each group of transposases in the genome.

In some cases a small percentage of the ISs in a group are shorter than the rest (particularly partial elements such as those created by subsequent genome rearrangements). Thus, each transposase 1 to m has its window individually shortened to all possible lengths 1 to n while keeping the rest of the windows at the same length. The above calculation of likelihood is repeated under this assumption, and any changes that are found to improve the likelihood are performed. Since the number of shortened ISs is almost always a small fraction of the group, this tends to find the same solution as an exhaustive search of all nm window lengths, and does so much more efficiently.

Once the edge of conservation has been found, the edges were then checked for inverted repeats using a Smith-Waterman alignment between the regions surrounding either edge of an IS element. If inverted repeats are found that disagree with the putative edges, the edges are adjusted to a limited extent.

Single-copy IS elements were found separately by finding transposases that were not placed in multiple-copy groups, and checking for inverted repeats in the surrounding regions. A Smith-Waterman alignment of the upstream and reverse-complement of the downstream regions was used to recognize significant inverted repeats and thus possible edges, a method first developed by IScan.

Once groups of IS elements were identified, a sample IS element from the set is selected, and blastn (NCBI) was used against the genome sequence to identify missing and partial copies of the IS element.

The family and group of each IS element is identified using blastp on the identified IS element ORFs against the ISFinder database, and classifying based on the family and group of the best match.

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